SECRET CINEMA

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Super interesting article – this is the first I have heard of supervised hackings of the government! To your question on how to protect the hackers, my recommendation is to hire dedicated hacker teams for the various branches of the government. These hackers would work independently / outside of the formal organizations, but they would have to go through intense background checks, be included in employee databases, and routinely reviewed to ensure (1) their own safety/security, and (2) the safety/security of the government agency.

On November 15, 2018, SECRET CINEMA commented on 3D Printing Better Surgery at the Mayo Clinic :

Really interesting article! One area where medical professionals should (and I believe are currently) innovate in 3D printing is organ creation. Patients can spend years on transplant lists for critical organs (hearts, kidneys, livers, etc.); if these organs could be printed-and effectively replicate the role of natural organs-this would allow patients to bypass waiting lists and more immediately receive life-saving transplant surgeries.

On November 15, 2018, SECRET CINEMA commented on Challenge.Gov – A Model for Government Crowdsourcing :

Devin raises a great point on “opening the flood gates”… What would you recommend as best practices for monitoring, reviewing, quality checking, and ultimately selecting the best ideas that emerge from crowd sourcing? How can you ensure that the review process is thorough, equitable, and that you maintain trust in the content producers by ensuring that the winning idea is ultimately implemented?

Overall, interesting read, and I loved the anecdote from Mexico City on improving bus route information!

On November 15, 2018, SECRET CINEMA commented on Printing the Future of Athletic Shoes at Adidas :

Fascinating post! To demonstrate the power of 3D printed, customized shoes, I would focus on two go-to-market strategies:
1) A-list athlete brand ambassadors – partner with top top-performing athletes to highlight the innovative, scientific, performance qualities of the shoes
2) Create “Adidas Labs” in flagship stores where regular customers can have their feet measured and can customize designs – brick & mortar/in-person human-human service is a great way to control the experience and the process of rolling out the technology.

Great insights on Sephora! My thoughts on the future of retail vs. digital…. Brick & mortar has a big role to play in Sephora’s future, but the key is quality over quantity. My guess is that not all of the 2,300 stores worldwide boast the same quality of in-store experience, so I would advocate to consolidating brick & mortar to flagship cities, key emerging markets, and travel retail POS’s (ex. airports). At each of these locations, I would integrate AI experiences (like those detailed above) so create the best possible physical experience of the brand for the consumer. A great quote to keep in mind from our Kiehl’s case in Marketing is “the store is the new media, and the media is the new store.” In other words, Sephora should rely on brick & mortar as its #1 marketing effort to consumers, and rely on its digital channels for sales.

On November 15, 2018, SECRET CINEMA commented on ML and Chill: Machine learning at Netflix :

Thank you for the post! If Netflix’s machine learning algorithms use past data on consumer behavior to promote related content on the site–as well as to produce new, related content–does an issue arise where the same people are always are watching the same content? Ultimately, does this result in a lack of diversity in (1) the types of content an individual is viewing and (2) the types of content that Netflix is incentivized to create? I’m curious the extent to which human judgement (i.e. the judgement to produce diverse, original, and even provocative content) is allowed to override data via machine learning to drive Netflix’s content strategy.

On November 15, 2018, SECRET CINEMA commented on ML and Chill: Machine learning at Netflix :

Very interesting read! If Netflix’s machine learning algorithms use past data on consumer behavior to promote related content on the site–as well as to produce new, related content–does an issue arise where the same people are always are watching the same content? Ultimately, does this result in a lack of diversity in (1) the types of content an individual is viewing and (2) the types of content that Netflix is incentivized to create? I’m curious the extent to which human judgement (i.e. the judgement to produce diverse, original, and even provocative content) is allowed to override data via machine learning to drive Netflix’s content strategy.